Data generated by geographical position systems (GPS) is currently sold in bulk, by the number of data points per day or per month. Generally, this data may be packaged in different ways—for example, in the form of “raw” or unprocessed probe data points, or in the form of processed probe data that reflects traffic speed on a roadway network. Ingests of raw probe data include data points of which many will not be relevant to the purchaser, and there is no current methodology for evaluating how much data in a bulk dataset of raw probe data is pertinent from the collection of information provided by each vendor. Similarly, there is no existing framework in the existing art for determining the value of a data point in a dataset that can be used to comparatively evaluate different vendors.
Raw probe data is useful for extracting information about traffic conditions on roadways, such as for example vehicular speed. Once a subscription to bulk raw probe data from a set (N) of vendors is undertaken, however, there is no current methodology for determining how much further value each additional vendor (N+1) provides for improving the analysis of roadway conditions like traffic flow from speed. In other words, there is no known framework in existence that permits traffic engineers to judge whether the accuracy of data extracted from a GPS dataset can be improved by additional subscriptions to vended probe data.
Additionally, there is no current methodology for performing a real-time evaluation of raw probe data to enable a prediction of data quality and realize a distribution of value extracted from an analysis of the quality of data points in a dataset. Because of the large number of GPS devices in use today, a real-time tool for foreseeing future roadway conditions such as traffic flow from known data would have significant utility in the marketplace, and would enable monetization of the value embedded within datasets comprised of raw probe data.